Blockchain for artificial intelligence training

ABSTRACT

An example operation may include one or more of dividing a neural network that corresponds to an artificial intelligence (AI) model into a plurality of sub-models, assigning the plurality of sub-models to a plurality of blockchain peers, respectively, training the sub-models, via the plurality of blockchain peers, to generate training results within an iteration, and committing the training results to a blockchain which is accessible by the plurality of blockchain peers.

BACKGROUND

A centralized platform stores and maintains data in a single location.This location is often a central computer, for example, a cloudcomputing environment, a web server, a mainframe computer, or the like.Information stored on a centralized platform is typically accessiblefrom multiple different points. Multiple users or client workstationscan work simultaneously on the centralized platform, for example, basedon a client/server configuration. A centralized platform is easy tomanage, maintain, and control, especially for purposes of securitybecause of its single location. Within a centralized platform, dataredundancy is minimized as a single storing place of all data alsoimplies that a given set of data only has one primary record.

SUMMARY

One example embodiment provides an apparatus that includes a processorconfigured to perform one or more of divide a neural network thatcorresponds to an artificial intelligence (AI) model into a plurality ofsub-models, assign the plurality of sub-models to a plurality ofblockchain peers, respectively, train the plurality of sub-models, viathe plurality of blockchain peers, to generate training results withinan iteration, and commit the training results to a blockchain which isaccessible by the plurality of blockchain peers.

Another example embodiment provides a method that includes one or moreof dividing a neural network that corresponds to an artificialintelligence (AI) model into a plurality of sub-models, assigning theplurality of sub-models to a plurality of blockchain peers,respectively, training the sub-models, via the plurality of blockchainpeers, to generate training results within an iteration, and committingthe training results to a blockchain which is accessible by theplurality of blockchain peers.

A further example embodiment provides a non-transitory computer-readablemedium comprising instructions, that when read by a processor, cause theprocessor to perform one or more of dividing a neural network thatcorresponds to an artificial intelligence (AI) model into a plurality ofsub-models, assigning the plurality of sub-models to a plurality ofblockchain peers, respectively, training the sub-models, via theplurality of blockchain peers, to generate training results within aniteration, and committing the training results to a blockchain which isaccessible by the plurality of blockchain peers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a blockchain network for iterativetraining of an artificial intelligence model according to exampleembodiments.

FIG. 2A is a diagram illustrating an example blockchain architectureconfiguration, according to example embodiments.

FIG. 2B is a diagram illustrating a blockchain transactional flow amongnodes, according to example embodiments.

FIG. 3A is a diagram illustrating a permissioned network, according toexample embodiments.

FIG. 3B is a diagram illustrating another permissioned network,according to example embodiments.

FIG. 3C is a diagram illustrating a permissionless network, according toexample embodiments.

FIG. 4A is a diagram illustrating a process of storing training data ofa plurality of sub-models on a blockchain according to exampleembodiments.

FIG. 4B is a diagram illustrating a process of cross-validating thetraining of the plurality of sub-models according to exampleembodiments.

FIG. 5 is a diagram illustrating a method of training an artificialintelligence model via blockchain, according to example embodiments.

FIG. 6A is a diagram illustrating an example system configured toperform one or more operations described herein, according to exampleembodiments.

FIG. 6B is a diagram illustrating another example system configured toperform one or more operations described herein, according to exampleembodiments.

FIG. 6C is a diagram illustrating a further example system configured toutilize a smart contract, according to example embodiments.

FIG. 6D is a diagram illustrating yet another example system configuredto utilize a blockchain, according to example embodiments.

FIG. 7A is a diagram illustrating a process of a new block being addedto a distributed ledger, according to example embodiments.

FIG. 7B is a diagram illustrating data contents of a new data block,according to example embodiments.

FIG. 7C is a diagram illustrating a blockchain for digital content,according to example embodiments.

FIG. 7D is a diagram illustrating a block which may represent thestructure of blocks in the blockchain, according to example embodiments.

FIG. 8A is a diagram illustrating an example blockchain which storesmachine learning (artificial intelligence) data, according to exampleembodiments.

FIG. 8B is a diagram illustrating an example quantum-secure blockchain,according to example embodiments.

FIG. 9 is a diagram illustrating an example system that supports one ormore of the example embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Thus, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined or removed in any suitablemanner in one or more embodiments. For example, the usage of the phrases“example embodiments”, “some embodiments”, or other similar language,throughout this specification refers to the fact that a particularfeature, structure, or characteristic described in connection with theembodiment may be included in at least one embodiment. Thus, appearancesof the phrases “example embodiments”, “in some embodiments”, “in otherembodiments”, or other similar language, throughout this specificationdo not necessarily all refer to the same group of embodiments, and thedescribed features, structures, or characteristics may be combined orremoved in any suitable manner in one or more embodiments. Further, inthe diagrams, any connection between elements can permit one-way and/ortwo-way communication even if the depicted connection is a one-way ortwo-way arrow. Also, any device depicted in the drawings can be adifferent device. For example, if a mobile device is shown sendinginformation, a wired device could also be used to send the information.

In addition, while the term “message” may have been used in thedescription of embodiments, the application may be applied to many typesof networks and data. Furthermore, while certain types of connections,messages, and signaling may be depicted in exemplary embodiments, theapplication is not limited to a certain type of connection, message, andsignaling.

Example embodiments provide methods, systems, components, non-transitorycomputer readable media, devices, and/or networks, which are directed toiteratively training an artificial intelligence (AI) model or ensembleof models using a blockchain as an infrastructure for executing thetraining. In some embodiments, the AI model may be a machine learningmodel, or the like.

According to various aspects, an AI model may be broken-up (e.g., split,divided, decomposed, etc.) into a plurality of sub-models by a gatewaysystem (e.g., a blockchain client, etc.) As an example, the AI model maybe a neural network that represents a mathematical function withparameters that are iteratively learned. When an iteration of the AImodel is executed (i.e., a training iteration), the parameters maydynamically change as a result of the training. The gateway system maybreak-up the neural network into smaller subsets of neurons (e.g.,sub-models) within the neural network, and assign the sub-models todifferent blockchain peers such that each peer only executes a portionof the AI model, but not the entire AI model. As a non-limiting example,each layer in a deep learning neural network may be a sub-model, and maybe assigned to a different blockchain peer (or peers) for training.

During training, each blockchain peer executes its correspondingsub-model based on a training data that may be provided from the gatewaysystem and stored on the blockchain. Here, each blockchain peer mayreceive a blockchain transaction which identifies the training data, thesub-model, and the like, which have already been submitted to theblockchain and/or the blockchain peers, and execute the sub-model basedon the blockchain transaction. The gateway system may store a blockchaintransaction which identifies training data/sub-model information foreach different peer. The results of the training (i.e., the change inparameters of the sub-model), may be stored by the blockchain peers onthe blockchain and collected by the gateway system. In response, thegateway system may determine whether the training was a success. If so,the gateway may prepare a next iteration of the training of the AImodel. The next iteration may include the same peers or different peersexecuting the respective sub-models. For example, the gateway may changethe assignments dynamically, per iteration, based on various factorssuch as system loads, randomly, etc.

The example embodiments improve upon the prior because the training ofthe AI model is broken up into sub-models and assigned to differentblockchain peers. As a result, the training is split-up among differentcomputing systems, creating a faster overall training execution.Furthermore, the individual results of the training may be recorded tothe blockchain after each iteration. This enables the gateway todetermine if an error has occurred in the training in real-time ratherthan wait for the model to finish. In particular, the gateway canidentify whether a training of a sub-model has resulted in faulty data,an error, etc. Furthermore, the blockchain can be used to keep animmutable/auditable record of the training of the different sub-models.Thus, if any questions arise about the validity of the AI model, theimmutable ledger including the blockchain can be used to provide proofthat the training did take place (e.g., the number of iterations and thetraining data satisfy predetermined thresholds, etc.).

In one embodiment this application utilizes a decentralized database(such as a blockchain) that is a distributed storage system, whichincludes multiple nodes that communicate with each other. Thedecentralized database includes an append-only immutable data structureresembling a distributed ledger capable of maintaining records betweenmutually untrusted parties. The untrusted parties are referred to hereinas peers or peer nodes. Each peer maintains a copy of the databaserecords and no single peer can modify the database records without aconsensus being reached among the distributed peers. For example, thepeers may execute a consensus protocol to validate blockchain storagetransactions, group the storage transactions into blocks, and build ahash chain over the blocks. This process forms the ledger by orderingthe storage transactions, as is necessary, for consistency. In variousembodiments, a permissioned and/or a permissionless blockchain can beused. In a public or permission-less blockchain, anyone can participatewithout a specific identity. Public blockchains can involve nativecryptocurrency and use consensus based on various protocols such asProof of Work (PoW). On the other hand, a permissioned blockchaindatabase provides secure interactions among a group of entities whichshare a common goal but which do not fully trust one another, such asbusinesses that exchange funds, goods, information, and the like.

This application can utilize a blockchain that operates arbitrary,programmable logic, tailored to a decentralized storage scheme andreferred to as “smart contracts” or “chaincodes.” In some cases,specialized chaincodes may exist for management functions and parameterswhich are referred to as system chaincode. The application can furtherutilize smart contracts that are trusted distributed applications whichleverage tamper-proof properties of the blockchain database and anunderlying agreement between nodes, which is referred to as anendorsement or endorsement policy. Blockchain transactions associatedwith this application can be “endorsed” before being committed to theblockchain while transactions, which are not endorsed, are disregarded.An endorsement policy allows chaincode to specify endorsers for atransaction in the form of a set of peer nodes that are necessary forendorsement. When a client sends the transaction to the peers specifiedin the endorsement policy, the transaction is executed to validate thetransaction. After validation, the transactions enter an ordering phasein which a consensus protocol is used to produce an ordered sequence ofendorsed transactions grouped into blocks.

This application can utilize nodes that are the communication entitiesof the blockchain system. A “node” may perform a logical function in thesense that multiple nodes of different types can run on the samephysical server. Nodes are grouped in trust domains and are associatedwith logical entities that control them in various ways. Nodes mayinclude different types, such as a client or submitting-client nodewhich submits a transaction-invocation to an endorser (e.g., peer), andbroadcasts transaction-proposals to an ordering service (e.g., orderingnode). Another type of node is a peer node which can receive clientsubmitted transactions, commit the transactions and maintain a state anda copy of the ledger of blockchain transactions. Peers can also have therole of an endorser, although it is not a requirement. Anordering-service-node or orderer is a node running the communicationservice for all nodes, and which implements a delivery guarantee, suchas a broadcast to each of the peer nodes in the system when committingtransactions and modifying a world state of the blockchain, which isanother name for the initial blockchain transaction which normallyincludes control and setup information.

This application can utilize a ledger that is a sequenced,tamper-resistant record of all state transitions of a blockchain. Statetransitions may result from chaincode invocations (i.e., transactions)submitted by participating parties (e.g., client nodes, ordering nodes,endorser nodes, peer nodes, etc.). Each participating party (such as apeer node) can maintain a copy of the ledger. A transaction may resultin a set of asset key-value pairs being committed to the ledger as oneor more operands, such as creates, updates, deletes, and the like. Theledger includes a blockchain (also referred to as a chain) which is usedto store an immutable, sequenced record in blocks. The ledger alsoincludes a state database which maintains a current state of theblockchain.

This application can utilize a chain that is a transaction log which isstructured as hash-linked blocks, and each block contains a sequence ofN transactions where N is equal to or greater than one. The block headerincludes a hash of the block's transactions, as well as a hash of theprior block's header. In this way, all transactions on the ledger may besequenced and cryptographically linked together. Accordingly, it is notpossible to tamper with the ledger data without breaking the hash links.A hash of a most recently added blockchain block represents everytransaction on the chain that has come before it, making it possible toensure that all peer nodes are in a consistent and trusted state. Thechain may be stored on a peer node file system (i.e., local, attachedstorage, cloud, etc.), efficiently supporting the append-only nature ofthe blockchain workload.

The current state of the immutable ledger represents the latest valuesfor all keys that are included in the chain transaction log. Since thecurrent state represents the latest key values known to a channel, it issometimes referred to as a world state. Chaincode invocations executetransactions against the current state data of the ledger. To make thesechaincode interactions efficient, the latest values of the keys may bestored in a state database. The state database may be simply an indexedview into the chain's transaction log, it can therefore be regeneratedfrom the chain at any time. The state database may automatically berecovered (or generated if needed) upon peer node startup, and beforetransactions are accepted.

Blockchain and artificial intelligence (AI) can mutually benefit eachother. On one hand, blockchain is not only a transaction processingplatform, but also a rich and reliable data store, especially longrunning and multi-functional blockchains. When an AI model is trainedbased on blockchain data, it becomes more reliable. On the other hand,AI helps the blockchain customer to gain more and better insights oftheir on-chain data.

However, there is still a gap between the AI workload and the blockchainplatform. Currently, AI workloads run on one platform while blockchainruns on another different platform. In this case, the communicationbetween the AI workloads and the blockchain is accomplished by APIsprovided by the blockchain platform.

This gap introduces a few problems. From an efficiency perspective, dataneeds to be transferred from the blockchain ledger to the platform wherethe AI workloads are running, which often incurs high costs. Also,because AI workloads are unable to understand the blockchain ledger,data from the blockchain ledger must be parsed/converted before beingfed to the AI workloads, which adds another layer of datatransformation. Also, from a security perspective, once data isoff-chain, it is no longer reliable. As a result, an AI workload couldbe defrauded with fake data. In this case, an AI model could becompromised in the middle of the training (e.g., fake training data,fraudulently skewed data, etc.), but the fraud will not be detected byexisting approaches until the AI model has been completely trained andtested or used. Thus the resources spent on training a compromised modelare wasted.

According to various embodiments, to make blockchain a more reliable andefficient infrastructure for an AI workload, the system hereinintroduces a blockchain gateway for AI. Multiple iterations aretypically needed for training an AI model workload. According to variousaspects, the gateway picks one iteration, divides a neural network (AImodel) into multiple sub-models, and creates a blockchain transaction totrain each sub-model. The transactions are submitted to the blockchainand simulated by different blockchain peers. Here, the gateway mayassign a different peer or group of peers to execute/train eachsub-model. The different blockchain peers store the results of thetraining, for example, the dynamically modified parameters of themathematical function of the AI algorithm that are changed by thetraining, the training data itself, the sub-model (algorithm) which wasexecuted, an identifier of the peer(s) that executed the sub-model, andthe like.

The blockchain peers may cross-validate the training results of theother peers via a consensus protocol. For example, a blockchain peer'swork can be validated by performing the same training operation andverifying the results match. Accordingly, the training process can bemonitored in real-time, and an alter will be triggered as soon as atransaction storing a training iteration fails. Furthermore, when atraining process takes many iterations, intermediate parameters producedby each iteration can be recorded on blockchain. There are two purposesto record these intermediate parameters, first for auditability of themodel, second for sharing the parameters to the subsequent trainingiterations. After that, the training for this iteration is finished, andthe gateway is notified to pick the next training iteration.

According to various embodiments, how the neural network is divided ineach training iteration by the gateway system can be adjusted flexiblyto best fit the current status of the blockchain peers. For example, ifsome peers become more powerful (have more available processing power,less of a load, etc.), the training of a larger portion of the neuralnetwork can be assigned to these peers. As another example, ifadditional peers join the blockchain, the neural network can be dividedinto more and smaller sub-models to further distribute the training tothe newly joined blockchain peers. Each peer may execute a smartcontract that has various phases, including reading an input, executingthe training on the sub-model based on the input, and committing theresults to the blockchain. Here, the input can be either the originaltraining data, or intermediate parameters generated by previous trainingtransactions of the sub-model or other sub-models. Sometimes a smartcontract may need to wait until some other sub-models are finishedexecuting during the training iteration.

The training of the AI model can be computationally heavy. Some of thebenefits of the example embodiment include alleviating the computationson a system. First, each blockchain peer may only train a sub-model ofthe AI model, which is less expensive than training the whole AI model.Second, training transactions may be assigned to peers based on theircapabilities. Third, the example embodiments enable a peer to offloadthe training to other computing nodes as long as the privacy of the datais taken care of.

The gateway system described herein enables the blockchain to be anefficient and reliable infrastructure to run AI workloads. The gatewaysystem may translate an AI training workload to blockchain transactionsand submit the transactions to a blockchain which is accessible to theblockchain peers. This allows training to happen on the blockchain viathe blockchain peers. It provides better training reliability by notonly recording the whole training process (i.e., updated parameters andtraining data at each iteration) on the blockchain, but also allowingreal-time training monitoring, so that no resource will be wasted totrain an already compromised model. Because the intermediate resultsbetween different training iterations are automatically shared by theblockchain, overhead of data transfer between the blockchain and anoff-chain platform is eliminated. Furthermore, the gateway system isable to assign training transactions by considering the heterogeneity ofdifferent peers which guarantees the appropriate utilization of thehardware resources.

FIG. 1 illustrates a blockchain network 100 for iterative training of anartificial intelligence model according to example embodiments.According to various embodiments, the blockchain network 100 may providean infrastructure for iteratively training an AI model. Here, the AImodel is represented by a neural network 120 which includes a pluralityof neurons having multiple layers 121, 122, and 123. In particular, theneural network 120 includes an input layer 121, an output layer 123, andone or more intermediate layers 122. For deep learning neural networks,the neural network 120 may include a plurality of intermediate (orhidden) layers 122. The neural network 120 may be stored by or otherwiseaccess by a gateway 110. The gateway 110 may be a server that functionsas a blockchain client, which submits transactions to a blockchain 140.

According to various embodiments, the gateway 110 is responsible to picka training iteration of the neural network 120 (which may includehundreds of training iterations or more), generate transactionscorresponding to this iteration. In some embodiments, the training dataand the sub-models may be stored on the blockchain (and the blockchainpeers) in advance. The gateway 110 is responsible for selecting whichpeers execute which sub-model during each iteration. Here, the gateway110 may submit the transactions to the blockchain 140 which identify thetraining data and sub-models to be executed by each of the peers, andcollect the results from blockchain 140. If the results are successful,the gateway 110 repeats the process, and picks the next trainingiteration. If the training is not successful, for example, a sub-modelcannot be executed or fraud is detected, the gateway 110 may modify oneor more of the blockchain peers performing the training, and repeat thetraining iteration.

To perform the training, a gateway 110 may decompose the neural network120 into smaller subsets of neurons 121, 122, and 123, at each iterationof the training of the neural network 120, and assign correspondingsub-models represented by the subset of neurons 121, 122, and 123 forexecution by blockchain peers 131, 132, and 133. Here, each subset 121,122, and 123 represents a sub-model of the AI model corresponding to theentire neural network 120. For example, the AI model may include analgorithm with multiple different functions. Each subset (sub-model) maybe a portion (e.g., one or more sub-portions) of the function.

In the example of FIG. 1, the gateway 110 breaks-up the neural network120 by layers. In this example, the input layer 121, the intermediatelayer 122, and the output layer 123 represent three different sub-modelsof the neural network 120. The gateway 110 may assign the threedifferent sub-models 121, 122, and 123 to three different blockchainpeers 131, 132, and 133, respectively. To do so, the gateway 110 maystore transactions on a blockchain 140 for each of the blockchain peers131, 132, and 133. The transactions may be is accessible to theblockchain peers 131, 132, and 133 which manage the blockchain 140.Here, the gateway 110 may function as a blockchain client, and submittransactions to the blockchain 140 via one or more of the blockchainpeers 131, 132, and 133.

The transactions may identify a sub-model, a peer to execute thesub-model, and the like. The transactions may also identify trainingdata that is to be executed during a next training iteration. Forexample, a blockchain transaction may identify a peer among theblockchain peers 131, 132, and 133 by an identifier (e.g., uniformresource locator, IP address, etc.), and may identify a training dataset, a function of the sub-model to be executed during the training, andthe like, which may already be stored by the blockchain peers 131, 132,and 133. Thus, the gateway 110 may provide the peers with the input forthe training along with the algorithm (sub-model) to be executed by thepeer during the training.

Each peer 131, 132, and 133 may read the transactions from theblockchain 140, identify the sub-models and the training data to beexecuted, and execute the respective sub-models based on the trainingdata identified in the transactions. The peers 131, 132, and 133 maystore the output of the training, the change in parameters of therespective sub-model as a result of the training, and the like, to theblockchain 140.

How the neural network 120 is divided can be flexible and dynamicallyconfigured for each training iteration, so as to best fit with thepotential changes of the peers such as changed in processing workload,availability, and the like. For example, the gateway 110 may considerassigning more training workloads to more powerful peers, divide theneural network 120 into more subsets (sub-models) when additional peersjoin the network, reduce the number of subsets when peers leave thenetwork, and the like.

Each of the blockchain peers 131, 132, and 133 may include a smartcontract that reads the blockchain transactions from the blockchain 140,and perform training based on the data therein. For example, the smartcontract may execute a sub-model on training data that are both includedin a blockchain transaction assigned to a corresponding peer. The smartcontract may write the results of the execution to the blockchainincluding the output from the executing and any changes to theparameters of the sub-model. For example, if the function is Ax+By=z,the changes to A and B may represent the changes to the parameters ofthe function x+y=z.

In some cases, a blockchain peer (e.g., the smart contract running thetraining) may need to wait until some other peers are finished executionof another sub-model. Thus, a first blockchain peer can wait for asecond blockchain peer to write the training results of a differentsub-model into the blockchain 140. Then, the first blockchain peer canuse the results of the second blockchain peer as an input into thetraining of a different sub-model. By storing the output of theexecution of a sub-model, the smart contract creates a reliable recordof the training process and shares the updated parameters of thesub-model with other peers which can then be used as the inputs of theirtraining process.

As will be appreciated, training an AI model is usually computationallyheavy, but it is less of a problem in the example embodiments becauseeach peer only works on training a sub-model, which is less expensivethan training the whole model. Also, as shown in FIG. 1, each sub-modelis assigned to one peer, but each sub-model may be assigned to a subsetof peers including more than one peer. In some embodiments, the gateway110 may assign different training transactions based on the capabilityof different peers. For example, peers with GPUs may be assigned moretraining transactions than those without. In some embodiments, a peercan offload the training to off-chain nodes if necessary. For example,training data may be transferred from a peer to an off-chain node (notshown). Here, the changes to the parameters may be transferred from theoff-chain node to the peer, and recorded to the blockchain 140.

The system described herein provides for an efficient and reliableinfrastructure for training an AI model using a blockchain, rather thanrelying on a combination of an off-chain platform and a blockchain. Someof the benefits of this system include on-chain training because datadoes not need to be moved off-chain, better training reliability becauseeach training iteration is recorded on the blockchain, real-timemonitoring at each iteration which eliminates wasted efforts to traincompromised AI models, better efficiency because the peers can bedynamically chosen at each iteration based on current conditions andavailability, flexible training assignments (e.g., a peer could beassigned a first sub-model during a first training iteration and then beassigned a second sub-model during a second training iteration, etc.)

FIG. 2A illustrates a blockchain architecture configuration 200,according to example embodiments. Referring to FIG. 2A, the blockchainarchitecture 200 may include certain blockchain elements, for example, agroup of blockchain nodes 202. The blockchain nodes 202 may include oneor more nodes 204-210 (these four nodes are depicted by example only).These nodes participate in a number of activities, such as blockchaintransaction addition and validation process (consensus). One or more ofthe blockchain nodes 204-210 may endorse transactions based onendorsement policy and may provide an ordering service for allblockchain nodes in the architecture 200. A blockchain node may initiatea blockchain authentication and seek to write to a blockchain immutableledger stored in blockchain layer 216, a copy of which may also bestored on the underpinning physical infrastructure 214. The blockchainconfiguration may include one or more applications 224 which are linkedto application programming interfaces (APIs) 222 to access and executestored program/application code 220 (e.g., chaincode, smart contracts,etc.) which can be created according to a customized configurationsought by participants and can maintain their own state, control theirown assets, and receive external information. This can be deployed as atransaction and installed, via appending to the distributed ledger, onall blockchain nodes 204-210.

The blockchain base or platform 212 may include various layers ofblockchain data, services (e.g., cryptographic trust services, virtualexecution environment, etc.), and underpinning physical computerinfrastructure that may be used to receive and store new transactionsand provide access to auditors which are seeking to access data entries.The blockchain layer 216 may expose an interface that provides access tothe virtual execution environment necessary to process the program codeand engage the physical infrastructure 214. Cryptographic trust services218 may be used to verify transactions such as asset exchangetransactions and keep information private.

The blockchain architecture configuration of FIG. 2A may process andexecute program/application code 220 via one or more interfaces exposed,and services provided, by blockchain platform 212. The code 220 maycontrol blockchain assets. For example, the code 220 can store andtransfer data, and may be executed by nodes 204-210 in the form of asmart contract and associated chaincode with conditions or other codeelements subject to its execution. As a non-limiting example, smartcontracts may be created to execute reminders, updates, and/or othernotifications subject to the changes, updates, etc. The smart contractscan themselves be used to identify rules associated with authorizationand access requirements and usage of the ledger. For example, the smartcontract (or chaincode executing the logic of the smart contract) mayread blockchain data 226 which may be processed by one or moreprocessing entities (e.g., virtual machines) included in the blockchainlayer 216 to generate results 228 including alerts, determiningliability, and the like, within a complex service scenario. The physicalinfrastructure 214 may be utilized to retrieve any of the data orinformation described herein.

A smart contract may be created via a high-level application andprogramming language, and then written to a block in the blockchain. Thesmart contract may include executable code which is registered, stored,and/or replicated with a blockchain (e.g., distributed network ofblockchain peers). A transaction is an execution of the smart contractlogic which can be performed in response to conditions associated withthe smart contract being satisfied. The executing of the smart contractmay trigger a trusted modification(s) to a state of a digital blockchainledger. The modification(s) to the blockchain ledger caused by the smartcontract execution may be automatically replicated throughout thedistributed network of blockchain peers through one or more consensusprotocols.

The smart contract may write data to the blockchain in the format ofkey-value pairs. Furthermore, the smart contract code can read thevalues stored in a blockchain and use them in application operations.The smart contract code can write the output of various logic operationsinto one or more blocks within the blockchain. The code may be used tocreate a temporary data structure in a virtual machine or othercomputing platform. Data written to the blockchain can be public and/orcan be encrypted and maintained as private. The temporary data that isused/generated by the smart contract is held in memory by the suppliedexecution environment, then deleted once the data needed for theblockchain is identified.

A chaincode may include the code interpretation (e.g., the logic) of asmart contract. For example, the chaincode may include a packaged anddeployable version of the logic within the smart contract. As describedherein, the chaincode may be program code deployed on a computingnetwork, where it is executed and validated by chain validators togetherduring a consensus process. The chaincode may receive a hash andretrieve from the blockchain a hash associated with the data templatecreated by use of a previously stored feature extractor. If the hashesof the hash identifier and the hash created from the stored identifiertemplate data match, then the chaincode sends an authorization key tothe requested service. The chaincode may write to the blockchain dataassociated with the cryptographic details.

FIG. 2B illustrates an example of a blockchain transactional flow 250between nodes of the blockchain in accordance with an exampleembodiment. Referring to FIG. 2B, the transaction flow may include aclient node 260 transmitting a transaction proposal 291 to an endorsingpeer node 281. The endorsing peer 281 may verify the client signatureand execute a chaincode function to initiate the transaction. The outputmay include the chaincode results, a set of key/value versions that wereread in the chaincode (read set), and the set of keys/values that werewritten in chaincode (write set). Here, the endorsing peer 281 maydetermine whether or not to endorse the transaction proposal. Theproposal response 292 is sent back to the client 260 along with anendorsement signature, if approved. The client 260 assembles theendorsements into a transaction payload 293 and broadcasts it to anordering service node 284. The ordering service node 284 then deliversordered transactions as blocks to all peers 281-283 on a channel. Beforecommittal to the blockchain, each peer 281-283 may validate thetransaction. For example, the peers may check the endorsement policy toensure that the correct allotment of the specified peers have signed theresults and authenticated the signatures against the transaction payload293.

Referring again to FIG. 2B, the client node initiates the transaction291 by constructing and sending a request to the peer node 281, which isan endorser. The client 260 may include an application leveraging asupported software development kit (SDK), which utilizes an availableAPI to generate a transaction proposal. The proposal is a request toinvoke a chaincode function so that data can be read and/or written tothe ledger (i.e., write new key value pairs for the assets). The SDK mayserve as a shim to package the transaction proposal into a properlyarchitected format (e.g., protocol buffer over a remote procedure call(RPC)) and take the client's cryptographic credentials to produce aunique signature for the transaction proposal.

In response, the endorsing peer node 281 may verify (a) that thetransaction proposal is well formed, (b) the transaction has not beensubmitted already in the past (replay-attack protection), (c) thesignature is valid, and (d) that the submitter (client 260, in theexample) is properly authorized to perform the proposed operation onthat channel. The endorsing peer node 281 may take the transactionproposal inputs as arguments to the invoked chaincode function. Thechaincode is then executed against a current state database to producetransaction results including a response value, read set, and write set.However, no updates are made to the ledger at this point. In 292, theset of values, along with the endorsing peer node's 281 signature ispassed back as a proposal response 292 to the SDK of the client 260which parses the payload for the application to consume.

In response, the application of the client 260 inspects/verifies thesignatures of the endorsing peers and compares the proposal responses todetermine if the proposal response is the same. If the chaincode onlyqueried the ledger, the application would inspect the query response andwould typically not submit the transaction to the ordering node service284. If the client application intends to submit the transaction to theordering node service 284 to update the ledger, the applicationdetermines if the specified endorsement policy has been fulfilled beforesubmitting (i.e., did all peer nodes necessary for the transactionendorse the transaction). Here, the client may include only one ofmultiple parties to the transaction. In this case, each client may havetheir own endorsing node, and each endorsing node will need to endorsethe transaction. The architecture is such that even if an applicationselects not to inspect responses or otherwise forwards an unendorsedtransaction, the endorsement policy will still be enforced by peers andupheld at the commit validation phase.

After successful inspection, in step 293 the client 260 assemblesendorsements into a transaction proposal and broadcasts the transactionproposal and response within a transaction message to the ordering node284. The transaction 294 may contain the read/write sets, the endorsingpeer signatures and a channel ID. The ordering node 284 does not need toinspect the entire content of a transaction in order to perform itsoperation, instead the ordering node 284 may simply receive transactionsfrom all channels in the network, order them chronologically by channel,and create blocks of transactions per channel.

The blocks are delivered from the ordering node 284 to all peer nodes281-283 on the channel. The data section within the block may bevalidated to ensure an endorsement policy is fulfilled and to ensurethat there have been no changes to ledger state for read set variablessince the read set was generated by the transaction 294 execution.Furthermore, in step 295 each peer node 281-283 appends the block to thechannel's chain, and for each valid transaction the write sets arecommitted to current state database. An event may be emitted, to notifythe client application that the transaction (invocation) has beenimmutably appended to the chain, as well as to notify whether thetransaction was validated or invalidated.

FIG. 3A illustrates an example of a permissioned blockchain network 300,which features a distributed, decentralized peer-to-peer architecture.In this example, a blockchain user 302 may initiate a transaction to thepermissioned blockchain 304. In this example, the transaction can be adeploy, invoke, or query, and may be issued through a client-sideapplication leveraging an SDK, directly through an API, etc. Networksmay provide access to a regulator 306, such as an auditor. A blockchainnetwork operator 308 manages member permissions, such as enrolling theregulator 306 as an “auditor” and the blockchain user 302 as a “client”.An auditor could be restricted only to querying the ledger whereas aclient could be authorized to deploy, invoke, and query certain types ofchaincode.

A blockchain developer 310 can write chaincode and client-sideapplications. The blockchain developer 310 can deploy chaincode directlyto the network through an interface. To include credentials from atraditional data source 312 in chaincode, the developer 310 could use anout-of-band connection to access the data. In this example, theblockchain user 302 connects to the permissioned blockchain 304 througha peer node 314. Before proceeding with any transactions, the peer node314 retrieves the user's enrollment and transaction certificates from acertificate authority 316, which manages user roles and permissions. Insome cases, blockchain users must possess these digital certificates inorder to transact on the permissioned blockchain 304. Meanwhile, a userattempting to utilize chaincode may be required to verify theircredentials on the traditional data source 312. To confirm the user'sauthorization, chaincode can use an out-of-band connection to this datathrough a traditional processing platform 318.

FIG. 3B illustrates another example of a permissioned blockchain network320, which features a distributed, decentralized peer-to-peerarchitecture. In this example, a blockchain user 322 may submit atransaction to the permissioned blockchain 324. In this example, thetransaction can be a deploy, invoke, or query, and may be issued througha client-side application leveraging an SDK, directly through an API,etc. Networks may provide access to a regulator 326, such as an auditor.A blockchain network operator 328 manages member permissions, such asenrolling the regulator 326 as an “auditor” and the blockchain user 322as a “client”. An auditor could be restricted only to querying theledger whereas a client could be authorized to deploy, invoke, and querycertain types of chaincode.

A blockchain developer 330 writes chaincode and client-sideapplications. The blockchain developer 330 can deploy chaincode directlyto the network through an interface. To include credentials from atraditional data source 332 in chaincode, the developer 330 could use anout-of-band connection to access the data. In this example, theblockchain user 322 connects to the network through a peer node 334.Before proceeding with any transactions, the peer node 334 retrieves theuser's enrollment and transaction certificates from the certificateauthority 336. In some cases, blockchain users must possess thesedigital certificates in order to transact on the permissioned blockchain324. Meanwhile, a user attempting to utilize chaincode may be requiredto verify their credentials on the traditional data source 332. Toconfirm the user's authorization, chaincode can use an out-of-bandconnection to this data through a traditional processing platform 338.

In some embodiments, the blockchain herein may be a permissionlessblockchain. In contrast with permissioned blockchains which requirepermission to join, anyone can join a permissionless blockchain. Forexample, to join a permissionless blockchain a user may create apersonal address and begin interacting with the network, by submittingtransactions, and hence adding entries to the ledger. Additionally, allparties have the choice of running a node on the system and employingthe mining protocols to help verify transactions.

FIG. 3C illustrates a process 350 of a transaction being processed by apermissionless blockchain 352 including a plurality of nodes 354. Asender 356 desires to send payment or some other form of value (e.g., adeed, medical records, a contract, a good, a service, or any other assetthat can be encapsulated in a digital record) to a recipient 358 via thepermissionless blockchain 352. In one embodiment, each of the senderdevice 356 and the recipient device 358 may have digital wallets(associated with the blockchain 352) that provide user interfacecontrols and a display of transaction parameters. In response, thetransaction is broadcast throughout the blockchain 352 to the nodes 354.Depending on the blockchain's 352 network parameters the nodes verify360 the transaction based on rules (which may be pre-defined ordynamically allocated) established by the permissionless blockchain 352creators. For example, this may include verifying identities of theparties involved, etc. The transaction may be verified immediately or itmay be placed in a queue with other transactions and the nodes 354determine if the transactions are valid based on a set of network rules.

In structure 362, valid transactions are formed into a block and sealedwith a lock (hash). This process may be performed by mining nodes amongthe nodes 354. Mining nodes may utilize additional software specificallyfor mining and creating blocks for the permissionless blockchain 352.Each block may be identified by a hash (e.g., 256 bit number, etc.)created using an algorithm agreed upon by the network. Each block mayinclude a header, a pointer or reference to a hash of a previous block'sheader in the chain, and a group of valid transactions. The reference tothe previous block's hash is associated with the creation of the secureindependent chain of blocks.

Before blocks can be added to the blockchain, the blocks must bevalidated. Validation for the permissionless blockchain 352 may includea proof-of-work (PoW) which is a solution to a puzzle derived from theblock's header. Although not shown in the example of FIG. 3C, anotherprocess for validating a block is proof-of-stake. Unlike theproof-of-work, where the algorithm rewards miners who solve mathematicalproblems, with the proof of stake, a creator of a new block is chosen ina deterministic way, depending on its wealth, also defined as “stake.”Then, a similar proof is performed by the selected/chosen node.

With mining 364, nodes try to solve the block by making incrementalchanges to one variable until the solution satisfies a network-widetarget. This creates the PoW thereby ensuring correct answers. In otherwords, a potential solution must prove that computing resources weredrained in solving the problem. In some types of permissionlessblockchains, miners may be rewarded with value (e.g., coins, etc.) forcorrectly mining a block.

Here, the PoW process, alongside the chaining of blocks, makesmodifications of the blockchain extremely difficult, as an attacker mustmodify all subsequent blocks in order for the modifications of one blockto be accepted. Furthermore, as new blocks are mined, the difficulty ofmodifying a block increases, and the number of subsequent blocksincreases. With distribution 366, the successfully validated block isdistributed through the permissionless blockchain 352 and all nodes 354add the block to a majority chain which is the permissionlessblockchain's 352 auditable ledger. Furthermore, the value in thetransaction submitted by the sender 356 is deposited or otherwisetransferred to the digital wallet of the recipient device 358.

FIG. 4A illustrates a process 400A of storing training data of aplurality of sub-models on a blockchain 420 according to exampleembodiments. Referring to FIG. 4A, a gateway 410 may break-up an AImodel into sub-models and assign the sub-models to different peers 441,442, and 443 within a blockchain network. Although three peers areshown, it should be appreciated that many more peers may be used. Inthis example, an AI model is decomposed into three sub-models A, B, andC. Here, the gateway 410 assigns sub-model A to blockchain peer 441,assigns sub-model B to blockchain peer 442, and assigns sub-model C toblockchain peer 443. To perform the assignment, the gateway 410 submitstransactions 421, 422, and 423 to the blockchain 420, via a blockchainpeer (e.g., any of peers 441-443).

For example, transaction 421 may identify the blockchain peer 441 by aunique identifier (e.g., a URL, IP address, etc.), a function ofsub-model A, input data for training sub-model A (e.g., raw trainingdata and/or data from a previous iteration, etc.), and the like.Likewise, transaction 422 may identify blockchain peer 442 by a uniqueidentifier, a function of sub-model B, input data for training sub-modelB (e.g., raw training data and/or data from a previous iteration, etc.),and transaction 423 may identify blockchain peer 443 by a uniqueidentifier, a function of sub-model C, input data for training sub-modelC (e.g., raw training data and/or data from a previous iteration, etc.).

Each of the blockchain peers 441-443 may read the transactions 421-423from the blockchain 420 (e.g., via a smart contract) and identify atransaction that is directed thereto. For example, blockchain peer 441may identify transaction 421 that includes the unique identifier of theblockchain peer 441 and execute the sub-model A, using the trainingdata, to generate results from the training. Here, the results mayinclude the output of the sub-model A, the values of the parameters ofthe sub-model after training, and the like. The results may be stored bythe blockchain peer 441 on the blockchain 420 via a transaction 424.Furthermore, the results may be stored in a world state database 430(state database) that is also on the ledger with the blockchain 420. Thestate database 430 may be a key-value store, and the updated parametervalues of the sub-model A may be stored as key-value pairs in the statedatabase 430.

Blockchain peer 442 may read the data from transaction 422, executesub-model B, and store the results as a transaction 425 on theblockchain 420. Furthermore, blockchain peer 443 may read data fromtransaction 423, execute sub-model C, and store results as transaction426 on the blockchain 420. Also, the results may be stored in the statedatabase 430. Each of the peers 441, 442, and 443, may identify theexecution results and updated parameters of each of the other blockchainpeers, and if necessary, use the results as inputs during the trainingof a respective sub-model. For example, blockchain peer 443 may trainsub-model C based on the results 425 from the training of sub-model Bwhich may be inputs to the sub-model C during the training iteration.

FIG. 4B illustrates a process 400B of cross-validating the training ofthe plurality of sub-models according to example embodiments. Referringto FIG. 4B, the plurality of blockchain peers 441-443 may cross-validatethe execution results of each of the other peers. For example,blockchain peers 441 and 442 may validate the results of blockchain peer443. Likewise, the blockchain peers 442 and 443 may validate the resultsof blockchain peer 441, and blockchain peers 441 and 443 may validatethe results of blockchain peer 442. The validation may be performed viaa consensus where each peer provides a vote/endorsement of the executionresults of the other peers. The peers may verify that the updatedparameters are correct and that no failures or errors occurred duringexecution. For example, blockchain peers 441 and 442 may execute thesub-model C based on the training data provided to blockchain peer 443to detect whether duplicate results are achieved. That is, blockchainpeers 441 and 442 may execute sub-model C to ensure that the sameresults are obtained as provided from blockchain peer 443 for trainingsub-model C. Thus, the peers can cross-validate each other's work.

In this case, the results of the cross-validation may be stored astransactions 427, 428, and 429, on the blockchain 420. The gateway 410may read the transactions 427, 428, and 429, and identify whether anyproblems occurred. In this case, transaction 429 indicates that the workfrom peer 443 (i.e., sub-model C) was not validated. Therefore, thegateway 410 may choose a different blockchain peer to perform theiteration of training of sub-model C.

FIG. 5 illustrates a method 500 of training an artificial intelligencemodel via blockchain, according to example embodiments. As anon-limiting example, the method 500 may be performed by a blockchainnetwork that includes one or more of a gateway system, a blockchain peeror peers, a blockchain ledger, and the like. Referring to FIG. 5, in510, the method 500 may include dividing a neural network correspondingto an artificial intelligence (AI) model into a plurality of sub-models,and in 520, assigning the plurality of sub-models to a plurality ofblockchain peers, respectively. Here, the gateway can decompose the AImodel into a plurality of subsets of neurons that are smaller than theentire AI model. As one example, for a deep learning neural network,each layer (e.g., input, output, intermediate/hidden, etc.) may be itsown sub-model. Each sub-model may be assigned to a different blockchainpeer or peers than the other sub-models. Thus, each peer may only trainone sub-model from the entire AI model.

In some embodiments, the dividing may include dynamically dividing theneural network into the plurality of sub-models based on processor loadsat the plurality of blockchain peers. For example, a sub-model may beassigned to a first blockchain peer during a first training iterationand dynamically re-assigned to a second blockchain peer (different fromthe first blockchain peer) during a second training iteration. Here, thegateway may detect that the second blockchain peer is now more suitablefor training the sub-model based on factors such as available load,bandwidth, hardware availability, security, and the like.

In 530, the method 500 may include training the plurality of sub-models,via the plurality of blockchain peers, to generate training resultswithin an iteration, and in 540, the method 500 may include committingthe training results to a blockchain which is accessible by theplurality of blockchain peers. In some embodiments, the generating mayinclude generating a training data set for a sub-model based on anoutput of a previous training iteration of the sub-model. In someembodiments, the providing may include transmitting one or moreblockchain requests that identify the training data and/or the sub-modelto be executed by a blockchain peer and storing the transaction to ablockchain that is accessible by the plurality of blockchain peers.

In some embodiments, the method 500 may further include detecting afailure of a sub-model as a result of the training iteration of theneural network, and in response, adjusting one or more of the trainingdata and a blockchain peer assigned to the sub-model for a nextiteration. In some embodiments, the method 500 may further includeexecuting the training data for training the plurality of sub-models viathe plurality of blockchain peers, and committing, via the blockchain,model parameters of the plurality of sub-models that result from theexecution. In some embodiments, the method 500 may further includecross-validating the model parameters of the plurality of sub-modelsthat result from the execution via the plurality of blockchain peers. Insome embodiments, the method 500 may further include generating a finaltraining result of the neural network via a combination of the trainingresults of the trained sub-models. In some embodiments, the training mayinclude generating intermediate model parameters within the iterationand the committing comprises committing the intermediate modelparameters to the blockchain.

FIG. 6A illustrates an example system 600 that includes a physicalinfrastructure 610 configured to perform various operations according toexample embodiments. Referring to FIG. 6A, the physical infrastructure610 includes a module 612 and a module 614. The module 614 includes ablockchain 620 and a smart contract 630 (which may reside on theblockchain 620), that may execute any of the operational steps 608 (inmodule 612) included in any of the example embodiments. Thesteps/operations 608 may include one or more of the embodimentsdescribed or depicted and may represent output or written informationthat is written or read from one or more smart contracts 630 and/orblockchains 620. The physical infrastructure 610, the module 612, andthe module 614 may include one or more computers, servers, processors,memories, and/or wireless communication devices. Further, the module 612and the module 614 may be a same module.

FIG. 6B illustrates another example system 640 configured to performvarious operations according to example embodiments. Referring to FIG.6B, the system 640 includes a module 612 and a module 614. The module614 includes a blockchain 620 and a smart contract 630 (which may resideon the blockchain 620), that may execute any of the operational steps608 (in module 612) included in any of the example embodiments. Thesteps/operations 608 may include one or more of the embodimentsdescribed or depicted and may represent output or written informationthat is written or read from one or more smart contracts 630 and/orblockchains 620. The physical infrastructure 610, the module 612, andthe module 614 may include one or more computers, servers, processors,memories, and/or wireless communication devices. Further, the module 612and the module 614 may be a same module.

FIG. 6C illustrates an example system configured to utilize a smartcontract configuration among contracting parties and a mediating serverconfigured to enforce the smart contract terms on the blockchainaccording to example embodiments. Referring to FIG. 6C, theconfiguration 650 may represent a communication session, an assettransfer session or a process or procedure that is driven by a smartcontract 630 which explicitly identifies one or more user devices 652and/or 656. The execution, operations and results of the smart contractexecution may be managed by a server 654. Content of the smart contract630 may require digital signatures by one or more of the entities 652and 656 which are parties to the smart contract transaction. The resultsof the smart contract execution may be written to a blockchain 620 as ablockchain transaction. The smart contract 630 resides on the blockchain620 which may reside on one or more computers, servers, processors,memories, and/or wireless communication devices.

FIG. 6D illustrates a system 660 including a blockchain, according toexample embodiments. Referring to the example of FIG. 6D, an applicationprogramming interface (API) gateway 662 provides a common interface foraccessing blockchain logic (e.g., smart contract 630 or other chaincode)and data (e.g., distributed ledger, etc.). In this example, the APIgateway 662 is a common interface for performing transactions (invoke,queries, etc.) on the blockchain by connecting one or more entities 652and 656 to a blockchain peer (i.e., server 654). Here, the server 654 isa blockchain network peer component that holds a copy of the world stateand a distributed ledger allowing clients 652 and 656 to query data onthe world state as well as submit transactions into the blockchainnetwork where, depending on the smart contract 630 and endorsementpolicy, endorsing peers will run the smart contracts 630.

The above embodiments may be implemented in hardware, in a computerprogram executed by a processor, in firmware, or in a combination of theabove. A computer program may be embodied on a computer readable medium,such as a storage medium. For example, a computer program may reside inrandom access memory (“RAM”), flash memory, read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), registers, hard disk, aremovable disk, a compact disk read-only memory (“CD-ROM”), or any otherform of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such thatthe processor may read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anapplication specific integrated circuit (“ASIC”). In the alternative,the processor and the storage medium may reside as discrete components.

FIG. 7A illustrates a process 700 of a new block being added to adistributed ledger 720, according to example embodiments, and FIG. 7Billustrates contents of a new data block structure 730 for blockchain,according to example embodiments. Referring to FIG. 7A, clients (notshown) may submit transactions to blockchain nodes 711, 712, and/or 713.Clients may be instructions received from any source to enact activityon the blockchain 720. As an example, clients may be applications thatact on behalf of a requester, such as a device, person or entity topropose transactions for the blockchain. The plurality of blockchainpeers (e.g., blockchain nodes 711, 712, and 713) may maintain a state ofthe blockchain network and a copy of the distributed ledger 720.Different types of blockchain nodes/peers may be present in theblockchain network including endorsing peers which simulate and endorsetransactions proposed by clients and committing peers which verifyendorsements, validate transactions, and commit transactions to thedistributed ledger 720. In this example, the blockchain nodes 711, 712,and 713 may perform the role of endorser node, committer node, or both.

The distributed ledger 720 includes a blockchain which stores immutable,sequenced records in blocks, and a state database 724 (current worldstate) maintaining a current state of the blockchain 722. Onedistributed ledger 720 may exist per channel and each peer maintains itsown copy of the distributed ledger 720 for each channel of which theyare a member. The blockchain 722 is a transaction log, structured ashash-linked blocks where each block contains a sequence of Ntransactions. Blocks may include various components such as shown inFIG. 7B. The linking of the blocks (shown by arrows in FIG. 7A) may begenerated by adding a hash of a prior block's header within a blockheader of a current block. In this way, all transactions on theblockchain 722 are sequenced and cryptographically linked togetherpreventing tampering with blockchain data without breaking the hashlinks. Furthermore, because of the links, the latest block in theblockchain 722 represents every transaction that has come before it. Theblockchain 722 may be stored on a peer file system (local or attachedstorage), which supports an append-only blockchain workload.

The current state of the blockchain 722 and the distributed ledger 722may be stored in the state database 724. Here, the current state datarepresents the latest values for all keys ever included in the chaintransaction log of the blockchain 722. Chaincode invocations executetransactions against the current state in the state database 724. Tomake these chaincode interactions extremely efficient, the latest valuesof all keys are stored in the state database 724. The state database 724may include an indexed view into the transaction log of the blockchain722, it can therefore be regenerated from the chain at any time. Thestate database 724 may automatically get recovered (or generated ifneeded) upon peer startup, before transactions are accepted.

Endorsing nodes receive transactions from clients and endorse thetransaction based on simulated results. Endorsing nodes hold smartcontracts which simulate the transaction proposals. When an endorsingnode endorses a transaction, the endorsing nodes creates a transactionendorsement which is a signed response from the endorsing node to theclient application indicating the endorsement of the simulatedtransaction. The method of endorsing a transaction depends on anendorsement policy which may be specified within chaincode. An exampleof an endorsement policy is “the majority of endorsing peers mustendorse the transaction”. Different channels may have differentendorsement policies. Endorsed transactions are forward by the clientapplication to ordering service 710.

The ordering service 710 accepts endorsed transactions, orders them intoa block, and delivers the blocks to the committing peers. For example,the ordering service 710 may initiate a new block when a threshold oftransactions has been reached, a timer times out, or another condition.In the example of FIG. 7A, blockchain node 712 is a committing peer thathas received a new data new data block 730 for storage on blockchain720. The first block in the blockchain may be referred to as a genesisblock which includes information about the blockchain, its members, thedata stored therein, etc.

The ordering service 710 may be made up of a cluster of orderers. Theordering service 710 does not process transactions, smart contracts, ormaintain the shared ledger. Rather, the ordering service 710 may acceptthe endorsed transactions and specifies the order in which thosetransactions are committed to the distributed ledger 720. Thearchitecture of the blockchain network may be designed such that thespecific implementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.)becomes a pluggable component.

Transactions are written to the distributed ledger 720 in a consistentorder. The order of transactions is established to ensure that theupdates to the state database 724 are valid when they are committed tothe network. Unlike a cryptocurrency blockchain system (e.g., Bitcoin,etc.) where ordering occurs through the solving of a cryptographicpuzzle, or mining, in this example the parties of the distributed ledger720 may choose the ordering mechanism that best suits that network.

When the ordering service 710 initializes a new data block 730, the newdata block 730 may be broadcast to committing peers (e.g., blockchainnodes 711, 712, and 713). In response, each committing peer validatesthe transaction within the new data block 730 by checking to make surethat the read set and the write set still match the current world statein the state database 724. Specifically, the committing peer candetermine whether the read data that existed when the endorserssimulated the transaction is identical to the current world state in thestate database 724. When the committing peer validates the transaction,the transaction is written to the blockchain 722 on the distributedledger 720, and the state database 724 is updated with the write datafrom the read-write set. If a transaction fails, that is, if thecommitting peer finds that the read-write set does not match the currentworld state in the state database 724, the transaction ordered into ablock will still be included in that block, but it will be marked asinvalid, and the state database 724 will not be updated.

Referring to FIG. 7B, a new data block 730 (also referred to as a datablock) that is stored on the blockchain 722 of the distributed ledger720 may include multiple data segments such as a block header 740, blockdata 750 (block data section), and block metadata 760. It should beappreciated that the various depicted blocks and their contents, such asnew data block 730 and its contents, shown in FIG. 7B are merelyexamples and are not meant to limit the scope of the exampleembodiments. In a conventional block, the data section may storetransactional information of N transaction(s) (e.g., 1, 10, 100, 500,1000, 2000, 3000, etc.) within the block data 750.

The new data block 730 may include a link to a previous block (e.g., onthe blockchain 722 in FIG. 7A) within the block header 740. Inparticular, the block header 740 may include a hash of a previousblock's header. The block header 740 may also include a unique blocknumber, a hash of the block data 750 of the new data block 730, and thelike. The block number of the new data block 730 may be unique andassigned in various orders, such as an incremental/sequential orderstarting from zero.

According to various embodiments, the block data 750 may store traininginformation 752 that is associated with the training of an AI modelbeing trained via the blockchain 730. For example, the traininginformation 752 may include a blockchain transaction generated by agateway system of the blockchain which includes a sub-model, trainingdata for the sub-model, and a peer ID of a peer who is to execute thesub-model using the training data. As another example, the traininginformation 752 may include the results of training a sub-model that arestored by a blockchain peer. For example, the results may include thechange to the parameters of the AI model's algorithm as a result of thetraining data and the sub-model being executed. According to variousembodiments, the training information 752 can be stored in an immutablelog of blocks on the distributed ledger 720. Some of the benefits ofstoring the training information 752 on the blockchain are reflected inthe various embodiments disclosed and depicted herein. Although in FIG.7B, the training information 752 is depicted in the block data 750, inother embodiments, the training information 752 may be located in theblock header 740 or the block metadata 760.

The block metadata 760 may store multiple fields of metadata (e.g., as abyte array, etc.). Metadata fields may include signature on blockcreation, a reference to a last configuration block, a transactionfilter identifying valid and invalid transactions within the block, lastoffset persisted of an ordering service that ordered the block, and thelike. The signature, the last configuration block, and the orderermetadata may be added by the ordering service 710. Meanwhile, acommitter of the block (such as blockchain node 712) may addvalidity/invalidity information based on an endorsement policy,verification of read/write sets, and the like. The transaction filtermay include a byte array of a size equal to the number of transactionsthat are included in the block data 750 and a validation codeidentifying whether a transaction was valid/invalid.

FIG. 7C illustrates an embodiment of a blockchain 770 for digitalcontent in accordance with the embodiments described herein. The digitalcontent may include one or more files and associated information. Thefiles may include media, images, video, audio, text, links, graphics,animations, web pages, documents, or other forms of digital content. Theimmutable, append-only aspects of the blockchain serve as a safeguard toprotect the integrity, validity, and authenticity of the digitalcontent, making it suitable use in legal proceedings where admissibilityrules apply or other settings where evidence is taken into considerationor where the presentation and use of digital information is otherwise ofinterest. In this case, the digital content may be referred to asdigital evidence.

The blockchain may be formed in various ways. In one embodiment, thedigital content may be included in and accessed from the blockchainitself. For example, each block of the blockchain may store a hash valueof reference information (e.g., header, value, etc.) along theassociated digital content. The hash value and associated digitalcontent may then be encrypted together. Thus, the digital content ofeach block may be accessed by decrypting each block in the blockchain,and the hash value of each block may be used as a basis to reference aprevious block. This may be illustrated as follows:

Block 1 Block 2 . . . Block N Hash Value 1 Hash Value 2 Hash Value NDigital Content 1 Digital Content 2 Digital Content N

In one embodiment, the digital content may be not included in theblockchain. For example, the blockchain may store the encrypted hashesof the content of each block without any of the digital content. Thedigital content may be stored in another storage area or memory addressin association with the hash value of the original file. The otherstorage area may be the same storage device used to store the blockchainor may be a different storage area or even a separate relationaldatabase. The digital content of each block may be referenced oraccessed by obtaining or querying the hash value of a block of interestand then looking up that has value in the storage area, which is storedin correspondence with the actual digital content. This operation may beperformed, for example, a database gatekeeper. This may be illustratedas follows:

Blockchain Storage Area Block 1 Hash Value Block 1 Hash Value . . .Content . . . . . . Block N Hash Value Block N Hash Value . . . Content

In the example embodiment of FIG. 7C, the blockchain 770 includes anumber of blocks 778 ₁, 778 ₂, . . . 778 _(N) cryptographically linkedin an ordered sequence, where N≥1. The encryption used to link theblocks 778 ₁, 778 ₂, . . . 778 _(N) may be any of a number of keyed orun-keyed Hash functions. In one embodiment, the blocks 778 ₁, 778 ₂, . .. 778 _(N) are subject to a hash function which produces n-bitalphanumeric outputs (where n is 256 or another number) from inputs thatare based on information in the blocks. Examples of such a hash functioninclude, but are not limited to, a SHA-type (SHA stands for Secured HashAlgorithm) algorithm, Merkle-Damgard algorithm, HAIFA algorithm,Merkle-tree algorithm, nonce-based algorithm, and anon-collision-resistant PRF algorithm. In another embodiment, the blocks778 ₁, 778 ₂, . . . , 778 _(N) may be cryptographically linked by afunction that is different from a hash function. For purposes ofillustration, the following description is made with reference to a hashfunction, e.g., SHA-2.

Each of the blocks 778 ₁, 778 ₂, . . . , 778 _(N) in the blockchainincludes a header, a version of the file, and a value. The header andthe value are different for each block as a result of hashing in theblockchain. In one embodiment, the value may be included in the header.As described in greater detail below, the version of the file may be theoriginal file or a different version of the original file.

The first block 778 ₁ in the blockchain is referred to as the genesisblock and includes the header 772 ₁, original file 774 ₁, and an initialvalue 776 ₁. The hashing scheme used for the genesis block, and indeedin all subsequent blocks, may vary. For example, all the information inthe first block 778 ₁ may be hashed together and at one time, or each ora portion of the information in the first block 778 ₁ may be separatelyhashed and then a hash of the separately hashed portions may beperformed.

The header 772 ₁ may include one or more initial parameters, which, forexample, may include a version number, timestamp, nonce, rootinformation, difficulty level, consensus protocol, duration, mediaformat, source, descriptive keywords, and/or other informationassociated with original file 774 ₁ and/or the blockchain. The header772 ₁ may be generated automatically (e.g., by blockchain networkmanaging software) or manually by a blockchain participant. Unlike theheader in other blocks 778 ₂ to 778 _(N) in the blockchain, the header772 ₁ in the genesis block does not reference a previous block, simplybecause there is no previous block.

The original file 774 ₁ in the genesis block may be, for example, dataas captured by a device with or without processing prior to itsinclusion in the blockchain. The original file 774 ₁ is received throughthe interface of the system from the device, media source, or node. Theoriginal file 774 ₁ is associated with metadata, which, for example, maybe generated by a user, the device, and/or the system processor, eithermanually or automatically. The metadata may be included in the firstblock 778 ₁ in association with the original file 774 ₁.

The value 776 ₁ in the genesis block is an initial value generated basedon one or more unique attributes of the original file 774 ₁. In oneembodiment, the one or more unique attributes may include the hash valuefor the original file 774 ₁, metadata for the original file 774 ₁, andother information associated with the file. In one implementation, theinitial value 776 ₁ may be based on the following unique attributes:

1) SHA-2 computed hash value for the original file

2) originating device ID

3) starting timestamp for the original file

4) initial storage location of the original file

5) blockchain network member ID for software to currently control theoriginal file and associated metadata

The other blocks 778 ₂ to 778 _(N) in the blockchain also have headers,files, and values. However, unlike the first block 772 ₁, each of theheaders 772 ₂ to 772 _(N) in the other blocks includes the hash value ofan immediately preceding block. The hash value of the immediatelypreceding block may be just the hash of the header of the previous blockor may be the hash value of the entire previous block. By including thehash value of a preceding block in each of the remaining blocks, a tracecan be performed from the Nth block back to the genesis block (and theassociated original file) on a block-by-block basis, as indicated byarrows 780, to establish an auditable and immutable chain-of-custody.

Each of the header 772 ₂ to 772 _(N) in the other blocks may alsoinclude other information, e.g., version number, timestamp, nonce, rootinformation, difficulty level, consensus protocol, and/or otherparameters or information associated with the corresponding files and/orthe blockchain in general.

The files 774 ₂ to 774 _(N) in the other blocks may be equal to theoriginal file or may be a modified version of the original file in thegenesis block depending, for example, on the type of processingperformed. The type of processing performed may vary from block toblock. The processing may involve, for example, any modification of afile in a preceding block, such as redacting information or otherwisechanging the content of, taking information away from, or adding orappending information to the files.

Additionally, or alternatively, the processing may involve merelycopying the file from a preceding block, changing a storage location ofthe file, analyzing the file from one or more preceding blocks, movingthe file from one storage or memory location to another, or performingaction relative to the file of the blockchain and/or its associatedmetadata. Processing which involves analyzing a file may include, forexample, appending, including, or otherwise associating variousanalytics, statistics, or other information associated with the file.

The values in each of the other blocks 776 ₂ to 776 _(N) in the otherblocks are unique values and are all different as a result of theprocessing performed. For example, the value in any one blockcorresponds to an updated version of the value in the previous block.The update is reflected in the hash of the block to which the value isassigned. The values of the blocks therefore provide an indication ofwhat processing was performed in the blocks and also permit a tracingthrough the blockchain back to the original file. This tracking confirmsthe chain-of-custody of the file throughout the entire blockchain.

For example, consider the case where portions of the file in a previousblock are redacted, blocked out, or pixelated in order to protect theidentity of a person shown in the file. In this case, the blockincluding the redacted file will include metadata associated with theredacted file, e.g., how the redaction was performed, who performed theredaction, timestamps where the redaction(s) occurred, etc. The metadatamay be hashed to form the value. Because the metadata for the block isdifferent from the information that was hashed to form the value in theprevious block, the values are different from one another and may berecovered when decrypted.

In one embodiment, the value of a previous block may be updated (e.g., anew hash value computed) to form the value of a current block when anyone or more of the following occurs. The new hash value may be computedby hashing all or a portion of the information noted below, in thisexample embodiment.

-   -   a) new SHA-2 computed hash value if the file has been processed        in any way (e.g., if the file was redacted, copied, altered,        accessed, or some other action was taken)    -   b) new storage location for the file    -   c) new metadata identified associated with the file    -   d) transfer of access or control of the file from one blockchain        participant to another blockchain participant

FIG. 7D illustrates an embodiment of a block which may represent thestructure of the blocks in the blockchain 790 in accordance with oneembodiment. The block, Block_(i), includes a header 772 _(i), a file 774_(i), and a value 776 _(i).

The header 772 _(i) includes a hash value of a previous blockBlock_(i-1) and additional reference information, which, for example,may be any of the types of information (e.g., header informationincluding references, characteristics, parameters, etc.) discussedherein. All blocks reference the hash of a previous block except, ofcourse, the genesis block. The hash value of the previous block may bejust a hash of the header in the previous block or a hash of all or aportion of the information in the previous block, including the file andmetadata.

The file 774 _(i) includes a plurality of data, such as Data 1, Data 2,. . . , Data N in sequence. The data are tagged with Metadata 1,Metadata 2, . . . , Metadata N which describe the content and/orcharacteristics associated with the data. For example, the metadata foreach data may include information to indicate a timestamp for the data,process the data, keywords indicating the persons or other contentdepicted in the data, and/or other features that may be helpful toestablish the validity and content of the file as a whole, andparticularly its use a digital evidence, for example, as described inconnection with an embodiment discussed below. In addition to themetadata, each data may be tagged with reference REF₁, REF₂, . . . ,REF_(N) to a previous data to prevent tampering, gaps in the file, andsequential reference through the file.

Once the metadata is assigned to the data (e.g., through a smartcontract), the metadata cannot be altered without the hash changing,which can easily be identified for invalidation. The metadata, thus,creates a data log of information that may be accessed for use byparticipants in the blockchain.

The value 776 _(i) is a hash value or other value computed based on anyof the types of information previously discussed. For example, for anygiven block Block_(i), the value for that block may be updated toreflect the processing that was performed for that block, e.g., new hashvalue, new storage location, new metadata for the associated file,transfer of control or access, identifier, or other action orinformation to be added. Although the value in each block is shown to beseparate from the metadata for the data of the file and header, thevalue may be based, in part or whole, on this metadata in anotherembodiment.

Once the blockchain 770 is formed, at any point in time, the immutablechain-of-custody for the file may be obtained by querying the blockchainfor the transaction history of the values across the blocks. This query,or tracking procedure, may begin with decrypting the value of the blockthat is most currently included (e.g., the last (N^(th)) block), andthen continuing to decrypt the value of the other blocks until thegenesis block is reached and the original file is recovered. Thedecryption may involve decrypting the headers and files and associatedmetadata at each block, as well. Decryption is performed based on thetype of encryption that took place in each block. This may involve theuse of private keys, public keys, or a public key-private key pair. Forexample, when asymmetric encryption is used, blockchain participants ora processor in the network may generate a public key and private keypair using a predetermined algorithm. The public key and private key areassociated with each other through some mathematical relationship.

The public key may be distributed publicly to serve as an address toreceive messages from other users, e.g., an IP address or home address.The private key is kept secret and used to digitally sign messages sentto other blockchain participants. The signature is included in themessage so that the recipient can verify using the public key of thesender. This way, the recipient can be sure that only the sender couldhave sent this message.

Generating a key pair may be analogous to creating an account on theblockchain, but without having to actually register anywhere. Also,every transaction that is executed on the blockchain is digitally signedby the sender using their private key. This signature ensures that onlythe owner of the account can track and process (if within the scope ofpermission determined by a smart contract) the file of the blockchain.

FIGS. 8A and 8B illustrate additional examples of use cases forblockchain which may be incorporated and used herein. In particular,FIG. 8A illustrates an example 800 of a blockchain 810 which storesmachine learning (artificial intelligence) data. Machine learning relieson vast quantities of historical data (or training data) to buildpredictive models for accurate prediction on new data. Machine learningsoftware (e.g., neural networks, etc.) can often sift through millionsof records to unearth non-intuitive patterns.

In the example of FIG. 8A, a host platform 820 builds and deploys amachine learning model for predictive monitoring of assets 830. Here,the host platform 820 may be a cloud platform, an industrial server, aweb server, a personal computer, a user device, and the like. Assets 830can be any type of asset (e.g., machine or equipment, etc.) such as anaircraft, locomotive, turbine, medical machinery and equipment, oil andgas equipment, boats, ships, vehicles, and the like. As another example,assets 830 may be non-tangible assets such as stocks, currency, digitalcoins, insurance, or the like.

The blockchain 810 can be used to significantly improve both a trainingprocess 802 of the machine learning model and a predictive process 804based on a trained machine learning model. For example, in 802, ratherthan requiring a data scientist/engineer or other user to collect thedata, historical data may be stored by the assets 830 themselves (orthrough an intermediary, not shown) on the blockchain 810. This cansignificantly reduce the collection time needed by the host platform 820when performing predictive model training. For example, using smartcontracts, data can be directly and reliably transferred straight fromits place of origin to the blockchain 810. By using the blockchain 810to ensure the security and ownership of the collected data, smartcontracts may directly send the data from the assets to the individualsthat use the data for building a machine learning model. This allows forsharing of data among the assets 830.

The collected data may be stored in the blockchain 810 based on aconsensus mechanism. The consensus mechanism pulls in (permissionednodes) to ensure that the data being recorded is verified and accurate.The data recorded is time-stamped, cryptographically signed, andimmutable. It is therefore auditable, transparent, and secure. AddingIoT devices which write directly to the blockchain can, in certain cases(i.e. supply chain, healthcare, logistics, etc.), increase both thefrequency and accuracy of the data being recorded.

Furthermore, training of the machine learning model on the collecteddata may take rounds of refinement and testing by the host platform 820.Each round may be based on additional data or data that was notpreviously considered to help expand the knowledge of the machinelearning model. In 802, the different training and testing steps (andthe data associated therewith) may be stored on the blockchain 810 bythe host platform 820. Each refinement of the machine learning model(e.g., changes in variables, weights, etc.) may be stored on theblockchain 810. This provides verifiable proof of how the model wastrained and what data was used to train the model. Furthermore, when thehost platform 820 has achieved a finally trained model, the resultingmodel may be stored on the blockchain 810.

After the model has been trained, it may be deployed to a liveenvironment where it can make predictions/decisions based on theexecution of the final trained machine learning model. For example, in804, the machine learning model may be used for condition-basedmaintenance (CBM) for an asset such as an aircraft, a wind turbine, ahealthcare machine, and the like. In this example, data fed back fromthe asset 830 may be input the machine learning model and used to makeevent predictions such as failure events, error codes, and the like.Determinations made by the execution of the machine learning model atthe host platform 820 may be stored on the blockchain 810 to provideauditable/verifiable proof. As one non-limiting example, the machinelearning model may predict a future breakdown/failure to a part of theasset 830 and create alert or a notification to replace the part. Thedata behind this decision may be stored by the host platform 820 on theblockchain 810. In one embodiment the features and/or the actionsdescribed and/or depicted herein can occur on or with respect to theblockchain 810.

New transactions for a blockchain can be gathered together into a newblock and added to an existing hash value. This is then encrypted tocreate a new hash for the new block. This is added to the next list oftransactions when they are encrypted, and so on. The result is a chainof blocks that each contain the hash values of all preceding blocks.Computers that store these blocks regularly compare their hash values toensure that they are all in agreement. Any computer that does not agree,discards the records that are causing the problem. This approach is goodfor ensuring tamper-resistance of the blockchain, but it is not perfect.

One way to game this system is for a dishonest user to change the listof transactions in their favor, but in a way that leaves the hashunchanged. This can be done by brute force, in other words by changing arecord, encrypting the result, and seeing whether the hash value is thesame. And if not, trying again and again and again until it finds a hashthat matches. The security of blockchains is based on the belief thatordinary computers can only perform this kind of brute force attack overtime scales that are entirely impractical, such as the age of theuniverse. By contrast, quantum computers are much faster (1000s of timesfaster) and consequently pose a much greater threat.

FIG. 8B illustrates an example 850 of a quantum-secure blockchain 852which implements quantum key distribution (QKD) to protect against aquantum computing attack. In this example, blockchain users can verifyeach other's identities using QKD. This sends information using quantumparticles such as photons, which cannot be copied by an eavesdropperwithout destroying them. In this way, a sender and a receiver throughthe blockchain can be sure of each other's identity.

In the example of FIG. 8B, four users are present 854, 856, 858, and860. Each of pair of users may share a secret key 862 (i.e., a QKD)between themselves. Since there are four nodes in this example, sixpairs of nodes exists, and therefore six different secret keys 862 areused including QKD_(AB), QKD_(AC), QKD_(AD), QKD_(BC), QKD_(BD), andQKD_(CD). Each pair can create a QKD by sending information usingquantum particles such as photons, which cannot be copied by aneavesdropper without destroying them. In this way, a pair of users canbe sure of each other's identity.

The operation of the blockchain 852 is based on two procedures (i)creation of transactions, and (ii) construction of blocks that aggregatethe new transactions. New transactions may be created similar to atraditional blockchain network. Each transaction may contain informationabout a sender, a receiver, a time of creation, an amount (or value) tobe transferred, a list of reference transactions that justifies thesender has funds for the operation, and the like. This transactionrecord is then sent to all other nodes where it is entered into a poolof unconfirmed transactions. Here, two parties (i.e., a pair of usersfrom among 854-860) authenticate the transaction by providing theirshared secret key 862 (QKD). This quantum signature can be attached toevery transaction making it exceedingly difficult to tamper with. Eachnode checks their entries with respect to a local copy of the blockchain852 to verify that each transaction has sufficient funds. However, thetransactions are not yet confirmed.

Rather than perform a traditional mining process on the blocks, theblocks may be created in a decentralized manner using a broadcastprotocol. At a predetermined period of time (e.g., seconds, minutes,hours, etc.) the network may apply the broadcast protocol to anyunconfirmed transaction thereby to achieve a Byzantine agreement(consensus) regarding a correct version of the transaction. For example,each node may possess a private value (transaction data of thatparticular node). In a first round, nodes transmit their private valuesto each other. In subsequent rounds, nodes communicate the informationthey received in the previous round from other nodes. Here, honest nodesare able to create a complete set of transactions within a new block.This new block can be added to the blockchain 852. In one embodiment thefeatures and/or the actions described and/or depicted herein can occuron or with respect to the blockchain 852.

FIG. 9 illustrates an example system 900 that supports one or more ofthe example embodiments described and/or depicted herein. The system 900comprises a computer system/server 902, which is operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system/server 902 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system/server 902 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 902 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 9, computer system/server 902 in cloud computing node900 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 902 may include, but are notlimited to, one or more processors or processing units 904, a systemmemory 906, and a bus that couples various system components includingsystem memory 906 to processor 904.

The bus represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 902 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 902, and it includes both volatileand non-volatile media, removable and non-removable media. System memory906, in one embodiment, implements the flow diagrams of the otherfigures. The system memory 906 can include computer system readablemedia in the form of volatile memory, such as random-access memory (RAM)910 and/or cache memory 912. Computer system/server 902 may furtherinclude other removable/non-removable, volatile/non-volatile computersystem storage media. By way of example only, storage system 914 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus by one or more data media interfaces. As will be further depictedand described below, memory 906 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of various embodiments of the application.

Program/utility 916, having a set (at least one) of program modules 918,may be stored in memory 906 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 918 generally carry out the functionsand/or methodologies of various embodiments of the application asdescribed herein.

As will be appreciated by one skilled in the art, aspects of the presentapplication may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present application may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present application may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Computer system/server 902 may also communicate with one or moreexternal devices 920 such as a keyboard, a pointing device, a display922, etc.; one or more devices that enable a user to interact withcomputer system/server 902; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 902 to communicate withone or more other computing devices. Such communication can occur viaI/O interfaces 924. Still yet, computer system/server 902 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 926. As depicted, network adapter 926communicates with the other components of computer system/server 902 viaa bus. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 902. Examples include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Although an exemplary embodiment of at least one of a system, method,and non-transitory computer readable medium has been illustrated in theaccompanied drawings and described in the foregoing detaileddescription, it will be understood that the application is not limitedto the embodiments disclosed, but is capable of numerous rearrangements,modifications, and substitutions as set forth and defined by thefollowing claims. For example, the capabilities of the system of thevarious figures can be performed by one or more of the modules orcomponents described herein or in a distributed architecture and mayinclude a transmitter, receiver or pair of both. For example, all orpart of the functionality performed by the individual modules, may beperformed by one or more of these modules. Further, the functionalitydescribed herein may be performed at various times and in relation tovarious events, internal or external to the modules or components. Also,the information sent between various modules can be sent between themodules via at least one of: a data network, the Internet, a voicenetwork, an Internet Protocol network, a wireless device, a wired deviceand/or via plurality of protocols. Also, the messages sent or receivedby any of the modules may be sent or received directly and/or via one ormore of the other modules.

One skilled in the art will appreciate that a “system” could be embodiedas a personal computer, a server, a console, a personal digitalassistant (PDA), a cell phone, a tablet computing device, a smartphoneor any other suitable computing device, or combination of devices.Presenting the above-described functions as being performed by a“system” is not intended to limit the scope of the present applicationin any way but is intended to provide one example of many embodiments.Indeed, methods, systems and apparatuses disclosed herein may beimplemented in localized and distributed forms consistent with computingtechnology.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge-scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether but may comprise disparate instructions stored in differentlocations which, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, random access memory (RAM), tape, or any othersuch medium used to store data.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

It will be readily understood that the components of the application, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations.Thus, the detailed description of the embodiments is not intended tolimit the scope of the application as claimed but is merelyrepresentative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that theabove may be practiced with steps in a different order, and/or withhardware elements in configurations that are different than those whichare disclosed. Therefore, although the application has been describedbased upon these preferred embodiments, it would be apparent to those ofskill in the art that certain modifications, variations, and alternativeconstructions would be apparent.

While preferred embodiments of the present application have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the application is to be definedsolely by the appended claims when considered with a full range ofequivalents and modifications (e.g., protocols, hardware devices,software platforms etc.) thereto.

What is claimed is:
 1. An apparatus comprising: a processor configured to: divide a neural network that corresponds to an artificial intelligence (AI) model into a plurality of sub-models, assign the plurality of sub-models to a plurality of blockchain peers, respectively, train the plurality of sub-models, via the plurality of blockchain peers, to generate training results within an iteration, and commit the training results to a blockchain which is accessible by the plurality of blockchain peers.
 2. The apparatus of claim 1, wherein the processor is configured to split the neural network into a plurality of different subsets of neurons.
 3. The apparatus of claim 1, wherein the processor is configured to dynamically divide the neural network into the plurality of sub-models based on processor loads at the plurality of blockchain peers.
 4. The apparatus of claim 1, wherein the processor is configured to generate training results for a sub-model based on an output of a previous training iteration of the sub-model.
 5. The apparatus of claim 1, wherein the processor is configured to transmit a blockchain request to a blockchain peer which identifies a sub-model to be trained by the blockchain peer within the iteration.
 6. The apparatus of claim 1, wherein the processor is configured to generate a final training result of the neural network via a combination of the training results of the trained sub-models and commit the final training result to the blockchain.
 7. The apparatus of claim 1, wherein the processor is configured to generate model parameters for the for the plurality of sub-models via the plurality of blockchain peers, and commit, via the blockchain, the model parameters of the plurality of sub-models to the blockchain.
 8. The apparatus of claim 1, wherein the processor is further configured to cross-validate the training results of the plurality of sub-models via the plurality of blockchain peers.
 9. A method comprising: dividing a neural network that corresponds to an artificial intelligence (AI) model into a plurality of sub-models, assigning the plurality of sub-models to a plurality of blockchain peers, respectively; training the plurality of sub-models, via the plurality of blockchain peers, to generate training results within an iteration; and committing the training results to a blockchain which is accessible by the plurality of blockchain peers.
 10. The method of claim 9, wherein the dividing comprises splitting the neural network into a plurality of different subsets of neurons.
 11. The method of claim 9, wherein the dividing comprises dynamically dividing the neural network into the plurality of sub-models based on processor loads at the plurality of blockchain peers.
 12. The method of claim 9, wherein the training comprises generating training results for a sub-model based on an output of a previous training iteration of the sub-model.
 13. The method of claim 9, wherein the method further comprises transmitting a blockchain request to a blockchain peer which identifies a sub-model to be trained by the blockchain peer within the iteration.
 14. The method of claim 9, wherein the method further comprises generating a final training result of the neural network via a combination of the training results of the trained sub-models.
 15. The method of claim 9, wherein the training comprises generating intermediate model parameters within the iteration and the committing comprises committing the intermediate model parameters to the blockchain.
 16. The method of claim 9, wherein the method further comprises cross-validating the training results of the sub-models via the plurality of blockchain peers.
 17. A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform a method comprising: dividing a neural network that corresponds to an artificial intelligence (AI) model into a plurality of sub-models, assigning the plurality of sub-models to a plurality of blockchain peers, respectively; training the sub-models, via the plurality of blockchain peers, to generate training results within an iteration; and committing the training results to a blockchain which is accessible by the plurality of blockchain peers.
 18. The non-transitory computer-readable medium of claim 17, wherein the dividing comprises splitting the neural network into a plurality of different subsets of neurons.
 19. The non-transitory computer-readable medium of claim 17, wherein the dividing comprises dynamically dividing the neural network into the plurality of sub-models based on processor loads at the plurality of blockchain peers.
 20. The non-transitory computer-readable medium of claim 17, wherein the training comprises generating training results for a sub-model based on an output of a previous training iteration of the sub-model. 